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Arda Mamur

Publications and source records attributed to Arda Mamur.

2 recordsLinked to original sources

A Systematic Analysis of Live Intraoperative Surgical Expert Narrations

Manual annotation remains the principal bottleneck in surgical video understanding. Live intraoperative narration by the operating surgeon is a scalable alternative that requires no manual annotation, but the information it contains has not been characterized. We analyzed 13 endoscopic lumbar spine decompression procedures (13.2 h) by two senior spine surgeons, using time-synchronized close-mic German audio. An annotation-free pipeline (word-level speech recognition, deterministic correction of surgical-term errors, utterance fusion, LLM-based translation to English, and relevance filtering) converted speech into time-stamped procedural cues labelled with the surgical entities they reference and their speech act. Of 3,171 utterances, 1,201 (37.9%) were retained as procedural cues, while 57.6% were non-procedural and removed. Retained cues referenced instruments (60.7%), anatomy (48.0%) and surgical actions (57.1%), and 98.5% carried at least one of these. 39% of cues (472 of 1,201) were directives, mainly instrument requests and commands. All 467 manually verified instrument requests preceded the instrument's appearance in the field, by a median of 8.2 s. Domain-term transcription corruption affected 18.0% of retained cues. Live narration is thus a dense, annotation-free supervisory signal describing instruments, anatomy, actions and team coordination, but it is noisy and temporally displaced from the events it names, and this displacement must be modelled before safe downstream use.

cs.CV↗

EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding

Operating rooms (ORs) demand precise coordination among surgeons, nurses, and equipment in a fast-paced, occlusion-heavy environment, necessitating advanced perception models to enhance safety and efficiency. Existing datasets either provide partial egocentric views or sparse exocentric multi-view context, but do not explore the comprehensive combination of both. We introduce EgoExOR, the first OR dataset and accompanying benchmark to fuse first-person and third-person perspectives. Spanning 94 minutes (84,553 frames at 15 FPS) of two emulated spine procedures, Ultrasound-Guided Needle Insertion and Minimally Invasive Spine Surgery, EgoExOR integrates egocentric data (RGB, gaze, hand tracking, audio) from wearable glasses, exocentric RGB and depth from RGB-D cameras, and ultrasound imagery. Its detailed scene graph annotations, covering 36 entities and 22 relations (568,235 triplets), enable robust modeling of clinical interactions, supporting tasks like action recognition and human-centric perception. We evaluate the surgical scene graph generation performance of two adapted state-of-the-art models and offer a new baseline that explicitly leverages EgoExOR's multimodal and multi-perspective signals. This new dataset and benchmark set a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception.

cs.CV↗